County and organizational predictors of depression symptoms among low-income nursing assistants in the USA.

Low-wage workers represent an ever-increasing proportion of the US workforce. A wide spectrum of firms demand low-wage workers, yet just 10 industries account for 70% of all low-paying jobs. The bulk of these jobs are in the services and retail sales industries. In health services, 60% of all workers are low-paid, with nursing aides, orderlies, personal attendants, and home care aides earning an average hourly wage of just 7.97 US dollars--a wage that keeps many of these workers hovering near or below the poverty line. Nursing assistants also tend to work in hazardous and grueling conditions. Work conditions are an important determinant of psychological well-being and mental disorders, particularly depression, in the workplace have important consequences for quality of life, worker productivity, and the utilization and cost of health care. In empirical studies of low-wage workers, county-level variables are of theoretical significance. Multilevel studies have recently provided evidence of a link between county-level variables and poor mental health among low-wage workers. To date, however, no studies have simultaneously considered the effect of county-and workplace-level variables. This study uses a repeated measures design and multilevel modeling to simultaneously test the effect of county-, organizational-, workplace-, and individual-level variables on depression symptoms among low-income nursing assistants employed in US nursing homes. We find that age and emotional strain have a statistically significant association with depression symptoms in this population, yet when controlling for county-level variables of poverty, the organizational-level variables used were no longer statistically significant predictors of depression symptoms. This study also contributes to current research methodology in the field of occupational health by using a cross-classified multilevel model to explicitly account for all variations in this three-level data structure, modeling and testing cross-classifications between nursing homes and counties of residence.